Neural-Control Family: What Deep Learning + Control Enables in the Real World
With the unprecedented advances of modern machine learning comes the tantalizing possibility of smart data-driven autonomous systems across a broad range of real-world settings. However, is machine learning (especially deep learning) really ready to be deployed in safety-critical systems?
While visiting Caltech, an aerospace director said:
I would love to incorporate deep learning into the design, manufacturing, and operations of our aircraft. But I need some guarantees.
Such a concern is definitely not unfounded, because the aerospace industry has spent over 60 years making the airplane safer and safer such that the modern airplane is one of the safest transportation methods. In a recent talk “Can We Really Use Machine Learning in Safety-Critical Systems?” at UCLA IPAM, Prof. Richard Murray discussed the number of deaths from transportation every miles in the U.S.:
| Human-driven car | Buses and trains | Airplane | Self-driving car |
| 7 | 0.1-0.4 | 0.07 | ? |
Based on this analysis, if I travel from LA to San Francisco, on average, taking a flight is 100 times safer than driving myself (also faster). Moreover, the above table is begging the following question: For deep-learning-based autonomous systems, how do we ensure a comparable level of safety to human or classic methods while maintaining advantages from deep learning?
To make progress on this challenge, I would like to present a class of learning-based control methods called Neural-Control Family, where deep-learning-based autonomous systems not only achieve exciting new capabilities and better performance than classic methods but also enjoy formal guarantees for safety and robustness. Here are some demonstrations, where all robots are running deep neural networks onboard in real-time:
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| Neural-Lander |
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| Neural-Swarm |
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